基于强化学习的知识图谱推理研究综述

Review of reinforcement learning based knowledge graph reasoning research

知识推理作为知识图谱补全中的一项重要任务,受到了学术界的广泛关注。为了提高模型的推理效果和可解释性,将强化学习与知识推理的结合是一种可行的解决方法。基于强化学习的知识推理方法将知识图谱研究的问题建模成路径或序列决策问题,能够更好地利用实体、关系等语义信息来提高推理效果和可解释性。首先,对知识图谱和知识推理的基本概念进行了叙述,阐述了近年来的研究进展。随后,从单层强化学习知识推理和双层强化学习知识推理两个角度,对基于强化学习的知识推理相关研究进行了分析与对比。最后,对知识推理如何应用于知识问答、智能推荐、医疗和交通等领域进行了探讨,并对基于强化学习的知识推理的未来研究方向进行了展望。

Knowledge reasoning is a fundamental task in knowledge graph completion.It is a popular topic in the academic community.Integrating reinforcement learning and knowledge reasoning is a viable solution to improve the inference effectiveness and interpretability of models.Taking the problem of knowledge graph research as a path or sequence decision problem in the knowledge reasoning method based on reinforcement learning can make better use of semantic information,such as entities and relationships,to improve reasoning effect and interpretability.This paper provided a descriptive overview of the basic concepts of knowledge graph and knowledge reasoning,and described the research progress in recent years.The paper analysed and compared the related research on knowledge reasoning based on reinforcement learning from two perspectives:single-layer and double-layer reinforcement learning knowledge reasoning.Furthermore,the paper explored the application of knowledge reasoning in various domains,including knowledge question answering,intelligent recommendation systems,healthcare,and transportation.Lastly,the paper discussed future research tendencies for reinforcement learning-based knowledge reasoning and offered insights into potential avenues for exploration and development.